The threat of asteroid impacts on Earth necessitates the detection and tracking of potentially hazardous asteroids to develop effective mitigation strategies. This PhD project aims to explore the combined use of the Vera Rubin Observatory (Legacy Survey of Space and Time - LSST) and the Global Fireball Observatory for identifying asteroids impacting the Earth. The upcoming Australian Dynamic REd All-sky Monitoring Survey (DREAMS)'s high-cadence near-infrared observations will open a new parameter space on the asteroid population. With proprietary access to these three world class facilities, the candidate for this project will be in a unique position to push our knowledge of asteroids in our Solar System, and specifically the ones that can threaten the Earth. The main aim of this PhD project is to advance our understanding of the asteroid population and its potential impact on Earth by using LSST and fireball networks to detect and track potentially hazardous asteroids. The secondary aim to to make use of cutting-edge data from the Australian DREAMS telescope infrared survey, a so far poorly explored parameter space for asteroid characterisation. This PhD project has significant scientific and practical significance. Scientifically, it will advance our understanding of the asteroid population and its potential impact on Earth, and it will contribute to efforts to protect Earth from potential asteroid impacts. Practically, it will develop and test new algorithms and software tools for analysing LSST and DREAMS data, which can be used by astronomers and planetary defence experts to detect and track potentially hazardous asteroids, and to develop and implement mitigation strategies. Overall, this project will contribute to the ongoing efforts to study asteroids and their potential impact on Earth, and to protect our planet from potential catastrophic events.

Asteroid impacts leave dust trails that can be visible for hours in the Earth's atmosphere, that can under some circumstances be detected by Earth observation satellites. This project will correlate known asteroid impacts with signatures in Earth observation data using both visible (RGB) and infrared wavelengths. This data will be used to learn about the asteroid impact process (dust production, energy deposition). Picture: dust trail from a superbolide over the Bering straight in 2018, imaged by the Himawari-8 weather satellite.

This project aims to significantly improve the efficiency and accuracy of drone-based meteorite detection in desert environments by transitioning from solely RGB imagery to multispectral data. Current methods, relying on RGB imagery and machine learning to identify dark rocks, suffer from a high rate of false positives, including spider holes, animal feces, and shadows. By incorporating multispectral imaging, we hope to be able to identify these sources of false positives which will enable more efficient meteorite recovery operations.

This project aims to develop an automated system for the detection and classification of fireballs using light curve data and associated attributes. By analysing features such as light curve duration, direction index, pixel displacement, and size, we will train machine learning models to distinguish genuine fireball events from other transient phenomena. Furthermore, we will explore the variability within fireball light curves to identify and characterize different types of fireballs, potentially revealing insights into their composition, velocity, and atmospheric interactions.

This project would search for specific seismic signatures in WA Array (GSWA) data for fireballs from spacecraft re-entries. By applying specialised filtering techniques to isolate the characteristic low-frequency acoustic-to-seismic coupled signals, this aims to create an automated detection algorithm that can identify these rare events amid background noise. This research will potentially offer a new method for monitoring uncontrolled spacecraft re-entries. The project leverages existing seismic infrastructure and combines geophysics and planetary science fields.

This project seeks to develop real-time synthetic tracking for Low-Earth Orbit (LEO) satellites using edge processing. Synthetic tracking is an image processing technique that increases the signal to noise of a moving object in astronomical image by stacking consecutive image while taking into account the object's motion. For detecting unknown objects, this becomes a computationally costly exercice to search the motion parameter space, well suited to GPU computing. This will leverage Curtin’s expertise in fast meteor tracking, Space Situational Awareness (SSA), and high-performance computing to overcome current limitations in detecting faint objects with SSA survey systems. Using GPUs to conduct synthetic tracking has been demonstrated offline on small datasets; this project will investigate the potential for real-time detection on edge processors.

The alignment of three astronomical bodies (e.g. an eclipse) is often an occasion to learn something new. When an asteroid is perfectly aligned with a star, the star disappears for a brief moment. This event can be recorded with an optical telescope well situated in the shadow path, and is a unique opportunity to learn about the size and shape of the asteroid. At radio wavelengths, asteroids can occult background radio sources (galaxies etc.), however the measurements are affected by Fresnel diffraction (illustrating figure from Lehtinen+ 2016). The lower the frequency, the stronger this effect becomes. This project aims to study the theoritical framework of low-frequency occultation events, identifying the feasability and expected results from undertaking and reducing such observations using the SKA-low and the MWA telescopes.

The Desert Fireball Network has been observing large shooting stars (fireball) at night time for nearly 10 years. 4 years ago it started also recording video data during the day. It is expected that once in a while a fireball will be visible during the day, however one issue is the number of false positives present amongst the detections. The student will look at fireball camera video clips (this is data nobody has looked at before), develop methods to classify the transients that have been recorded, and possibly identify some of these rare daytime fireballs!